Breaking Transferability of Adversarial Samples with RandomnessBreaking Transferability of Adversarial Samples with RandomnessZhou, Yan and Kantarcioglu, Murat and Xi, Bowei2018

Paper summarydavidstutzZhou et al. study transferability of adversarial examples against ensembles of randomly perturbed networks. Specifically, they consider randomly perturbing the weights using Gaussian additive noise. Using an ensemble of these perturbed networks, the authors show that transferability of adversarial examples decreases significantly. However, the authors do not consider adapting their attack to this defense scenario.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

Zhou et al. study transferability of adversarial examples against ensembles of randomly perturbed networks. Specifically, they consider randomly perturbing the weights using Gaussian additive noise. Using an ensemble of these perturbed networks, the authors show that transferability of adversarial examples decreases significantly. However, the authors do not consider adapting their attack to this defense scenario.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).